Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing

TL;DR

This paper introduces an interpretable deep learning framework for hyperspectral unmixing that effectively models nonlinearity and endmember variability, providing transparent insights and improved performance over existing methods.

Contribution

It presents a novel probabilistic variational deep learning approach with disentanglement for interpretable hyperspectral unmixing, addressing non-idealities and enhancing transparency.

Findings

Outperforms state-of-the-art algorithms on synthetic datasets.

Effectively models nonlinearity and endmember variability.

Provides high interpretability through structured probabilistic modeling.

Abstract

Although considerable effort has been dedicated to improving the solution to the hyperspectral unmixing problem, non-idealities such as complex radiation scattering and endmember variability negatively impact the performance of most existing algorithms and can be very challenging to address. Recently, deep learning-based frameworks have been explored for hyperspectral umixing due to their flexibility and powerful representation capabilities. However, such techniques either do not address the non-idealities of the unmixing problem, or rely on black-box models which are not interpretable. In this paper, we propose a new interpretable deep learning method for hyperspectral unmixing that accounts for nonlinearity and endmember variability. The proposed method leverages a probabilistic variational deep-learning framework, where disentanglement learning is employed to properly separate the abundances and endmembers. The model is learned end-to-end using stochastic backpropagation, and trained using a self-supervised strategy which leverages benefits from semi-supervised learning techniques. Furthermore, the model is carefully designed to provide a high degree of interpretability. This includes modeling the abundances as a Dirichlet distribution, the endmembers using low-dimensional deep latent variable representations, and using two-stream neural networks composed of additive piecewise-linear/nonlinear components. Experimental results on synthetic and real datasets illustrate the performance of the proposed method compared to state-of-the-art algorithms.